code: def _one_hot_tensor(vals, one_hot_dim=1, as_label=False):
      if not isinstance(vals, (torch.Tensor, np.ndarray)):
          raise ValueError(
              "Input to _one_hot_tensor must be tensor or np array."
          )
      if not vals.ndim == 1:
          raise ValueError("Input to _one_hot_tensor must be 1-D.")
      vals = torch.tensor(vals)
      if as_label:
          if vals.ndim > 1:
              return vals.squeeze()
          else:
              return vals
      one_hot = torch.zeros(len(vals), one_hot_dim)
      one_hot.scatter_(1, vals.unsqueeze(-1), 1.0)
      return one_hot
  
imports: import logging
  import networkx
  import numpy
  import torch
  import torch.nn
  from deepsnap.graph import Graph
  import graphgym.register
  from graphgym.config import cfg
  from graphgym.contrib.transform.identity import compute_identity
label_desc: Use image node_id 4 for calling the FeatureAugment obj's underlying member method code with example usage: obj._one_hot_tensor(vals, one_hot_dim, as_label) and returns: one_hot, vals, vals